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Beyond Citations: A Cross-Domain Metric for Dataset Impact and Shareability

Smitha Muthya Sudheendra, Zhongxing Zhang, Wenwen Cao, Jisu Huh, Jaideep Srivastava

TL;DR

The paper addresses the insufficiency of publication-centric metrics to capture dataset impact, proposing the X-index, a two-step metric that combines a dataset-level V-score (breadth of reuse, FAIR quality, citation impact, and transitive reuse) with an author-level aggregation. A concrete computational pipeline using OpenAlex, SerpAPI, and Google Scholar demonstrates feasibility on modest hardware, achieving rapid computation and scalability. Validation against expert judgments shows strong correlations (Spearman 0.95, R^2 0.94), supporting the approach's reliability. The X-index aims to incentivize open data sharing, cross-domain reuse, and fair credit for data-centric contributions across disciplines.

Abstract

The scientific community increasingly relies on open data sharing, yet existing metrics inadequately capture the true impact of datasets as research outputs. Traditional measures, such as the h-index, focus on publications and citations but fail to account for dataset accessibility, reuse, and cross-disciplinary influence. We propose the X-index, a novel author-level metric that quantifies the value of data contributions through a two-step process: (i) computing a dataset-level value score (V-score) that integrates breadth of reuse, FAIRness, citation impact, and transitive reuse depth, and (ii) aggregating V-scores into an author-level X-index. Using datasets from computational social science, medicine, and crisis communication, we validate our approach against expert ratings, achieving a strong correlation. Our results demonstrate that the X-index provides a transparent, scalable, and low-cost framework for assessing data-sharing practices and incentivizing open science. The X-index encourages sustainable data-sharing practices and gives institutions, funders, and platforms a tangible way to acknowledge the lasting influence of research datasets.

Beyond Citations: A Cross-Domain Metric for Dataset Impact and Shareability

TL;DR

The paper addresses the insufficiency of publication-centric metrics to capture dataset impact, proposing the X-index, a two-step metric that combines a dataset-level V-score (breadth of reuse, FAIR quality, citation impact, and transitive reuse) with an author-level aggregation. A concrete computational pipeline using OpenAlex, SerpAPI, and Google Scholar demonstrates feasibility on modest hardware, achieving rapid computation and scalability. Validation against expert judgments shows strong correlations (Spearman 0.95, R^2 0.94), supporting the approach's reliability. The X-index aims to incentivize open data sharing, cross-domain reuse, and fair credit for data-centric contributions across disciplines.

Abstract

The scientific community increasingly relies on open data sharing, yet existing metrics inadequately capture the true impact of datasets as research outputs. Traditional measures, such as the h-index, focus on publications and citations but fail to account for dataset accessibility, reuse, and cross-disciplinary influence. We propose the X-index, a novel author-level metric that quantifies the value of data contributions through a two-step process: (i) computing a dataset-level value score (V-score) that integrates breadth of reuse, FAIRness, citation impact, and transitive reuse depth, and (ii) aggregating V-scores into an author-level X-index. Using datasets from computational social science, medicine, and crisis communication, we validate our approach against expert ratings, achieving a strong correlation. Our results demonstrate that the X-index provides a transparent, scalable, and low-cost framework for assessing data-sharing practices and incentivizing open science. The X-index encourages sustainable data-sharing practices and gives institutions, funders, and platforms a tangible way to acknowledge the lasting influence of research datasets.

Paper Structure

This paper contains 10 sections, 2 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Proposed formulation
  4. Computational implementation
  5. Feasibility
  6. Impact
  7. Validation of Formula
  8. Discussion
  9. Conclusion
  10. Appendix A

Figures (3)

  • Figure 1: An illustration of our V-score and X-index pipeline.
  • Figure 2: Regression analysis: Geometric Mean Score
  • Figure 3: Regression analysis:V-score